Struct MLModel

pub struct MLModel {

    pub algorithm: Option<String>,
    pub compute_time: Option<i64>,
    pub created_at: Option<f64>,
    pub created_by_iam_user: Option<String>,
    pub endpoint_info: Option<RealtimeEndpointInfo>,
    pub finished_at: Option<f64>,
    pub input_data_location_s3: Option<String>,
    pub last_updated_at: Option<f64>,
    pub ml_model_id: Option<String>,
    pub ml_model_type: Option<String>,
    pub message: Option<String>,
    pub name: Option<String>,
    pub score_threshold: Option<f32>,
    pub score_threshold_last_updated_at: Option<f64>,
    pub size_in_bytes: Option<i64>,
    pub started_at: Option<f64>,
    pub status: Option<String>,
    pub training_data_source_id: Option<String>,
    pub training_parameters: Option<HashMap<String, String>>,
}

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

Fields

algorithm: Option<String>

The algorithm used to train the MLModel. The following algorithm is supported:

• SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

compute_time: Option<i64>

created_at: Option<f64>

The time that the MLModel was created. The time is expressed in epoch time.

created_by_iam_user: Option<String>

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

endpoint_info: Option<RealtimeEndpointInfo>

The current endpoint of the MLModel.

finished_at: Option<f64>

input_data_location_s3: Option<String>

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

last_updated_at: Option<f64>

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

ml_model_id: Option<String>

The ID assigned to the MLModel at creation.

ml_model_type: Option<String>

Identifies the MLModel category. The following are the available types:

• REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

• MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

message: Option<String>

A description of the most recent details about accessing the MLModel.

name: Option<String>

A user-supplied name or description of the MLModel.

score_threshold: Option<f32>

score_threshold_last_updated_at: Option<f64>

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

size_in_bytes: Option<i64>

started_at: Option<f64>

status: Option<String>

The current status of an MLModel. This element can have one of the following values:

• PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

• INPROGRESS - The creation process is underway.

• FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

• COMPLETED - The creation process completed successfully.

• DELETED - The MLModel is marked as deleted. It isn't usable.

training_data_source_id: Option<String>

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

training_parameters: Option<HashMap<String, String>>

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

• sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

  The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

• sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.

• sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

  The value is a double that ranges from 0 to MAXDOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

• sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.

  The value is a double that ranges from 0 to MAXDOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

Trait Implementations

impl Clone for MLModel

fn clone(&self) -> MLModel

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for MLModel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for MLModel

fn default() -> MLModel

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for MLModel

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for MLModel

fn eq(&self, other: &MLModel) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for MLModel